The rumour stance classification task consists in determining the type of support that each individual post expresses towards the disputed veracity of a rumour. The task is especially interesting in the context of Twitter, where unverified reports about breaking news are continually being posted and discussed as they unfold. This problem was originally tackled as a 2-way classification task, where each tweet was classified as supporting or denying a rumour. However, recent research [1]} found this categorisation to be insufficient to encompass all the different kinds of reactions to rumours, and a broader, 4-way classification task has been suggested instead. The argument behind this is that users in social media will not necessarily express a clear inclination towards supporting or denying a rumour, but can also be skeptical by posing questions about it or can make comments about the rumour that are unrelated to its disputed veracity. The four categories in the extended classification scheme thus include supporting, denying, querying and commenting. In this work we set out the rumour stance classification that adopts this broader scheme. We define the rumour stance classification task as follows: we assume we have a set \(D\)  of rumours \(R_i\) , each of which is composed of a collection of rumourous conversational threads. For simplicity here we refer to a rumour as the aggregate of its Twitter conversational threads, which is ultimately a collection of tweets. Each rumour has a variably sized set of tweets \(t_i\)  discussing it so that \(R_i = \lbrace t_1, ..., t_{|R_i|}\rbrace \) ; the task consists in determining the stance of each of the tweets \(t_j\)  pertaining to a new, unseen rumour \(R_i\)  as one of \(Y = \lbrace supporting, denying, querying, commenting\rbrace \) .
